RAG implementation for enterprise knowledge, built on retrieval that works
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RAG implementation, three pillars, one operator.
One Canadian team builds the RAG pipeline and measures whether it actually retrieves.
Ingestion and chunking
Structure-aware splitting, so a policy clause doesn't get cut across two chunks. Metadata travels with every chunk into the embeddings pipeline, because a passage with no source, no date, and no owner can't be cited or filtered later.
Retrieval and reranking
Hybrid search pairs semantic search with keyword matching, so part numbers and policy codes survive alongside meaning. A reranker orders what comes back, permission filters apply at query time, and the vector database is a decision we make late, once your data volume and residency needs are known.
Evaluation loop
Context recall and precision measured on real traffic, not a curated demo set. Change the chunking, watch the number move, keep it or revert it. That's the difference between a system and an opinion.
Does RAG Implementation work the same way in every industry?
No. The RAG Implementation work is the same craft in every sector, but the conditions around it are not. In financial services that means OSFI's technology and cyber risk rules; in government, Protected A, B and C data handling; and in defence and aerospace, controlled goods rules, CMMC and the flow-down from your prime. We keep the service and change the part that bends.
OSFI's technology and cyber risk rules set what an examiner will ask to see.
OSFI B-13. Technology and cyber risk across three domains: governance, technology operations and resilience, and cyber security. It binds federally regulated institutions, and only those.
Protected A, B and C data handling decides where the work can run and who can touch it.
Protected A, B and C. Each level changes where the data can physically live, who can reach it, and what you have to prove about both. Most files turn on Protected B, and most disagreements turn on what counts as reaching it.
Controlled goods rules, CMMC and the flow-down from your prime set what a supplier can do.
ITAR and the Controlled Goods Program. Registration under the Defence Production Act is mandatory before anyone examines, possesses or transfers controlled goods in Canada. Technical data counts, so a drawing sitting on a laptop is in scope.
Health information sits under provincial privacy law, and your IT provider is usually the custodian's agent.
PHIPA. Ontario's health privacy act, with its own definition of a custodian and its own duties for the agents acting on a custodian's behalf. Your IT provider is usually an agent.
Solicitor-client privilege decides how a firm can handle its data.
Law Society obligations. Confidentiality and competence duties reach the technology you practise with, and the vendors who touch client information. The firm is expected to have assessed the provider, which means the provider should arrive with the answers.
What do you need from RAG Implementation?
Tell us who you are and what you're working on. A senior engineer reads your message before replying, so the first answer is a real read on scope, risk and timing, within an hour, 24/7. Nothing here is a commitment, and we'll tell you plainly if we're not the right fit.
Your request
- Service
- RAG Implementation
What happens next
- A senior engineer replies within an hour, 24/7, never an SDR.
- We book a 30-minute call to listen, sketch options, and flag risks.
- You get a scoped statement of work within 3 business days.
Call the 24/7 line
+1 514-465-1584Enterprise RAG in Canada, made real.
Nearly every failed RAG project failed at retrieval. Almost none of them measured it.
Where enterprise RAG actually breaks.
Retrieval augmented generation isn't hard to demo and is hard to run. The demo works because twenty documents were hand-picked. Production breaks because forty thousand weren't. When RAG fails, retrieval is the failure point 73% of the time, not generation, and roughly 70% of teams running it in production have no systematic evaluation of retrieval quality at all (source: industry analyses, 2026). So they tune prompts and swap models while the actual defect sits one layer down, in what the system handed the model to read.
Three places a RAG architecture goes wrong.
The pipeline has parts and each one can break it. Risk sits in three places. Chunking by token count, which splits a clause from its condition and hands the model half a rule. A single dense vector search with no reranking and no keyword fallback, so exact terms like part numbers and policy codes get missed. And retrieval that ignores who's asking, which turns your knowledge base into a data leak wearing a helpful interface. A RAG architecture we build has hybrid retrieval, structure-aware chunking, a reranker, and permission filters at query time.
Why permissions and residency are the Canadian part.
Canadian context makes the permission layer non-negotiable. 19.2% of Canadian businesses now use AI in production, up from 12.2% a year earlier (source: Statistics Canada, 2026-05-27), and PIPEDA applies to every document your pipeline can reach. A RAG system inherits your existing access model exactly, so a knowledge base built over an estate with broad sharing will surface things nobody intended. Data residency matters too, and we can keep the vector database and the documents in Canada.
Want a Senior IT Consultant's Read on Your Setup?
Tell us what you run today and what keeps breaking. A senior SMEnode engineer replies within an hour with a first read on scope, risk and timing. No sales handoff.
Request a consultHow our RAG pipeline and vector database work runs.
Four steps, and the third one is where most engagements get honest. We build an evaluation set from your real questions before writing pipeline code, because without it whether the thing works becomes a matter of opinion and the loudest opinion wins. Once recall and precision are measurable, decisions get easy: change the chunking, see the number move, keep or revert. That loop is unglamorous and it's the entire difference between a demo and a system.
Engagement phases
Phases 01 to 04
Read the corpus
Document structure, formats, update frequency, and who's allowed to see what. Permissions before pipelines. If your access model is already too broad, we say so here rather than three months into a build.
Build the pipeline
Structure-aware chunking with metadata, embeddings, a vector store sized to your corpus, hybrid retrieval, reranking, and generation that cites its sources. Every stage is testable on its own, so a regression has an address.
Measure retrieval
Context recall and precision scored against your evaluation set. We report both numbers before you see a demo, because a demo that looks good on curated questions tells you nothing about the other thirty-nine thousand documents.
Run the eval loop
Ongoing scoring on live traffic, so quality drift shows up as a number instead of a complaint. When your documents change, the score moves first and someone can act on it before users lose trust in the answers.
RAG implementation questions, answered straight.
Answers first, including when you shouldn't build one. An architect takes the call.
Where this work connects.
These disciplines share the same senior team and tend to land together. Follow the thread to the next one.
- AIAI SecurityCanadian AI security that tests your models and pipelines, then fixes what breaks.
- AIAI Strategy ConsultingCanadian AI strategy consulting that scores use cases, kills the weak ones, and costs the rest.
- AIAI Automation ServicesCanadian AI automation built on real integrations, real permissions, and a plan for when it breaks.
